SoroushVahidi/scidocs huggingface dataset:
Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
(2016)

This paper introduces a probabilistic algorithm for multi-robot decision-making under uncertainty, which can be posed as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Dec-POMDPs are inherently synchronous decision-making frameworks which require significant computational resources to be solved, making them infeasible for many real-world robotics applications. The Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) was recently introduced as an extension of the Dec-POMDP that uses high-level macro-actions to allow large-scale, asynchronous decision-making. However, existing Dec-POSMDP solution methods have limited scalability or perform poorly as the problem size grows. This paper proposes a cross-entropy based Dec-POSMDP algorithm motivated by the combinatorial optimization literature. The algorithm is applied to a constrained package delivery domain, where it significantly outperforms existing Dec-POSMDP solution methods.
Challenges of using homomorphic encryption to secure cloud computing
(2015)

With the emergence of cloud computing, the concept of information security has become a major issue. Indeed, the security of such a system is the greatest concern of computer scientists, providers of cloud and organizations who want to adopt and benefit from these services. Cloud computing providers must implement concepts ensuring network security, hardware, data storage and strategies of control and access to services. All these elements help to preserve data security and ensuring the availability of services associated with the Cloud, to better satisfy clients and acquire and build their trust. However, even if the data storage security in cloud servers is assured, reluctance remain when it comes to process the confidential data. Indeed, the fear that sensitive data is being used is a major obstacle in the adoption of cloud services by enterprises. To overcome this obstacle, the use of methods that can perform operations on encrypted data without knowing the secret key, seems to be an effective way to strengthen the confidentiality of information. In this paper we will examine the challenges facing Homomorphic Encryption methods to allow suppliers of cloud to perform operations on encrypted data, and provide the same results after treatment, as if they were performing calculations on raw data.
Compact LTCC Bandpass Filter With Wide Stopband Using Discriminating Coupling
(2014)

This paper presents a novel compact low-temperature cofired ceramic (LTCC) bandpass filter (BPF) with wide stopband and high selectivity. The proposed circuit consists of two coupled λ<sub>g</sub>/4 transmission-line resonators. A special coupling region is selected to realize a novel discriminating coupling scheme for generating a transmission zero (TZ) at the third harmonic frequency. The mechanism is analyzed and the design guideline is described. The source-load coupling is introduced to generate two TZs near the passband and one in the stopband. Thus, wide stopband can be obtained without extra circuits. Due to the LTCC multilayer structures, the filter size is 0.058 λ<sub>g</sub>×0.058 λ<sub>g</sub>×0.011 λ<sub>g</sub>, or 2.63 mm × 2.61 mm × 0.5 mm. The simulated and measured results of the demonstrated LTCC BPF are presented to validate the proposed design.
Cluster-based Web Summarization
(2013)

We propose a novel approach to abstractive Web summarization based on the observation that summaries for similar URLs tend to be similar in both content and structure. We leverage existing URL clusters and construct per-cluster word graphs that combine known summaries while abstracting out URL-specific attributes. The resulting topology, conditioned on URL features, allows us to cast the summarization problem as a structured learning task using a lowest cost path search as the decoding step. Early experimental results on a large number of URL clusters show that this approach is able to outperform previously proposed Web summarizers.
Very-Heavy Sled Training for Improving Horizontal-Force Output in Soccer Players.
(2017)

BACKGROUND
Sprint running acceleration is a key feature of physical performance in team sports, and recent literature shows that the ability to generate large magnitudes of horizontal ground-reaction force and mechanical effectiveness of force application are paramount. The authors tested the hypothesis that very-heavy loaded sled sprint training would induce an improvement in horizontal-force production, via an increased effectiveness of application.


METHODS
Training-induced changes in sprint performance and mechanical outputs were computed using a field method based on velocity-time data, before and after an 8-wk protocol (16 sessions of 10- × 20-m sprints). Sixteen male amateur soccer players were assigned to either a very-heavy sled (80% body mass sled load) or a control group (unresisted sprints).


RESULTS
The main outcome of this pilot study is that very-heavy sled-resisted sprint training, using much greater loads than traditionally recommended, clearly increased maximal horizontal-force production compared with standard unloaded sprint training (effect size of 0.80 vs 0.20 for controls, unclear between-groups difference) and mechanical effectiveness (ie, more horizontally applied force; effect size of 0.95 vs -0.11, moderate between-groups difference). In addition, 5-m and 20-m sprint performance improvements were moderate and small for the very-heavy sled group and small and trivial for the control group, respectively. Practical Applications: This brief report highlights the usefulness of very-heavy sled (80% body mass) training, which may suggest value for practical improvement of mechanical effectiveness and maximal horizontal-force capabilities in soccer players and other team-sport athletes.


RESULTS
This study may encourage further research to confirm the usefulness of very-heavy sled in this context.
Analysis of malicious input issues on intelligent systems
(2014)

Intelligent systems can facilitate decision making and have been widely applied to various domains. The output of intelligent systems relies on the users’ input. However, with the development of Web-Based Interface, users can easily provide dishonest input. Therefore, the accuracy of the generated decision will be affected. This dissertation presents three essays to discuss the defense solutions for malicious input into three types of intelligent systems: expert systems, recommender systems, and rating systems. Different methods are proposed in each domain based on the nature of each problem. The first essay addresses the input distortion issue in expert systems. It develops four methods to distinguish liars from truth-tellers, and redesign the expert systems to control the impact of input distortion by liars. Experimental results show that the proposed methods could lead to the better accuracy or the lower misclassification cost. The second essay addresses the shilling attack issue in recommender systems. It proposes an integrated Value-based Neighbor Selection (VNS) approach, which aims to select proper neighbors for recommendation systems that maximize the e-retailer’s profit while protecting the system from shilling attacks. Simulations are conducted to demonstrate the effectiveness of the proposed method. The third essay addresses the rating fraud issue in rating systems. It designs a twophase procedure for rating fraud detection based on the temporal analysis on the rating series.
Why it can matter more than IQ
(2013)

Goleman highlights the many reports of the disintegration of civility and safety, and the onslaught of mean-spirited impulse running amok and claims that these only reflect back to us, on a larger scale, a creeping sense of emotions out of control in our own lives, and in the lives of those people living around us. He seeks to make sense of the senselessness and bring about change which will help our children fare better in life.
Learning decision trees from dynamic data streams
(2005)

This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, that works online, processing each example in constant time, and performing a single scan over the training examples. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. For multi-class problems the algorithm grows a binary tree for each possible pair of classes, leading to a forest of trees. Decision nodes and leaves contain naive-Bayes classifiers playing different roles during the induction process. Naive-Bayes in leaves are used to classify test examples, naive-Bayes in inner nodes can be used as multivariate splitting-tests if chosen by the splitting criteria, and used to detect drift in the distribution of the examples that traverse the node. When a drift is detected, all the sub-tree rooted at that node will be pruned. The use of naive-Bayes classifiers at leaves to classify test examples, the use of splitting-tests based on the outcome of naive-Bayes, and the use of naive-Bayes classifiers at decision nodes to detect drift are directly obtained from the sufficient statistics required to compute the splitting criteria, without no additional computations. This aspect is a main advantage in the context of high-speed data streams. This methodology was tested with artificial and real-world data sets. The experimental results show a very good performance in comparison to a batch decision tree learner, and high capacity to detect and react to drift.
Hockey Action Recognition via Integrated Stacked Hourglass Network
(2017)

A convolutional neural network (CNN) has been designed to interpret player actions in ice hockey video. The hourglass network is employed as the base to generate player pose estimation and layers are added to this network to produce action recognition. As such, the unified architecture is referred to as action recognition hourglass network, or ARHN. ARHN has three components. The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. Since no benchmark dataset for pose estimation or action recognition is available for hockey players, we generate such an annotated dataset. Experimental results show action recognition accuracy of 65% for four types of actions in hockey. When similar poses are merged to three and two classes, the accuracy rate increases to 71% and 78%, proving the efficacy of the methodology for automated action recognition in hockey.
A qualitative exploration of individuals' motivators for seeking substance user treatment.
(2012)

Substance abuse continues to greatly impact the US population. The purpose of this study is to improve understanding of the reasons why individuals seek substance user treatment. Qualitative interviews were conducted with 50 men (n = 25) and women (n = 25) across eight substance user treatment programs in Durham, North Carolina, in 2010. We identify six motivating themes regarding reasons for seeking treatment and explore how these themes vary by the participants' mode of entry into treatment. Findings provide a holistic understanding of how and why individuals come into treatment that can enhance the quality and impact of substance user services.
Learning Context for Text Categorization
(2011)

This paper describes our work which is based on discovering context for text document categorization. The document categorization approach is derived from a combination of a learning paradigm known as relation extraction and an technique known as context discovery. We demonstrate the effectiveness of our categorization approach using reuters 21578 dataset and synthetic real world data from sports domain. Our experimental results indicate that the learned context greatly improves the categorization performance as compared to traditional categorization approaches.
β-VAE : L EARNING B ASIC
(2017)

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β > 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.
Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting
(2017)

Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.
Childhood Obesity Perceptions Among African American Caregivers in a Rural Georgia Community: A Mixed Methods Approach
(2014)

Given the pivotal role of African American caregiver’s perceptions of childhood obesity in rural areas, the inclusion of caregiver’s perceptions could potentially reduce childhood obesity rates. The objective of the current study was to explore childhood obesity perceptions among African Americans in a rural Georgia community. This concurrent mixed methods study utilized two theoretical frameworks: Social Cognitive Theory and Social Ecological Model. Using a convenience sample, caregivers ages 22–65 years completed a paper-based survey (n = 135) and a face-to-face interview (n = 12) to explore perceptions of obesity risk factors, health complications, weight status, built environment features, and obesity prevention approaches. Descriptive statistics were generated and a six-step process was used for qualitative analysis. Participants commonly cited behavioral risk factors; yet, social aspects and appearance of the community were not considered contributing factors. Chronic diseases were reported as obesity health complications. Caregivers had a distorted view of their child’s weight status. In addition, analysis revealed that caregivers assessed child’s weight and height measurements by the child’s appearance or a recent doctor visit. Environmental barriers reported by caregivers included safety concerns and insufficient physical activity venues and programs. Also, caregivers conveyed parents are an imperative component of preventing obesity. Although this study found caregivers were aware of obesity risk factors, health complications, built environment features, and prevention approaches their obesity perceptions were not incorporated into school or community prevention efforts. Findings suggest that children residing in rural areas are in need of tailored efforts that address caregiver perceptions of obesity.
Balancing traffic load in wireless networks with curveball routing
(2007)

We address the problem of balancing the traffic load in multi-hop wireless networks. We consider a point-to-point communicating network with a uniform distribution of source-sink pairs. When routing along shortest paths, the nodes that are centrally located forward a disproportionate amount of traffic. This translates into increased congestion and energy consumption. However, the maximum load can be decreased if the packets follow curved paths. We show that the optimum such routing scheme can be expressed in terms of geometric optics and computed by linear programming. We then propose a practical solution, which we call Curveball Routing which achieves results not much worse than the optimum.
 We evaluate our solution at three levels of fidelity: a Java high-level simulator, the ns2 simulator, and the Intel Mirage Sensor Network Testbed. Simulation results using the high-level simulator show that our solution successfully avoids the crowded center of the network, and reduces the maximum load by up to 40%. At the same time, the increase of the expected path length is minimal, i.e., only 8% on average. Simulation results using the ns2 simulator show that our solution can increase throughput on moderately loaded networks by up to 15%, while testbed results show a reduction in peak energy usage by up to 25%. Our prototype suggests that our solution is easily deployable.
A decision support system for operations in a container terminal
(2005)

We describe a variety of inter-related decisions made during daily operations at a container terminal. The ultimate goal of these decisions is to minimize the berthing time of vessels, the resources needed for handling the workload, the waiting time of customer trucks, and the congestion on the roads and at the storage blocks and docks inside the terminal; and to make the best use of the storage space. Given the scale and complexity of these decisions, it is essential to use decision support tools to make them. This paper reports on work to develop such a decision support system (DSS). We discuss the mathematical models and algorithms used in designing the DSS, the reasons for using these approaches, and some experimental results. D 2003 Elsevier B.V. All rights reserved.
Reducing the human overhead in text categorization
(2006)

Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe away to reduce this effort, while retaining the methods' accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
(2007)

The typical CAPTCHA GUI consists of two parts: a character image with noise, and an and J. Saul, "Asirra: a CAPTCHA that exploits interest-aligned manual image categorization," in 14th ACM conference on Computer and Communications. CAPTCHAs (short for "Completely Automated Public Turing test to tell Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization. (30)J. Elson, J. R. Douceur, J. Howell, and J. Saul, “Asirra: A CAPTCHA that exploits interest-aligned manual image categorization,” in Proc. ACM CCS, 2007, pp.
On the Character of Phishing URLs: Accurate and Robust Statistical Learning Classifiers
(2015)

Phishing attacks resulted in an estimated $3.2 billion dollars worth of stolen property in 2007, and the success rate for phishing attacks is increasing each year [17]. Phishing attacks are becoming harder to detect and more elusive by using short time windows to launch attacks. In order to combat the increasing effectiveness of phishing attacks, we propose that combining statistical analysis of website URLs with machine learning techniques will give a more accurate classification of phishing URLs. Using a two-sample Kolmogorov-Smirnov test along with other features we were able to accurately classify 99.3% of our dataset, with a false positive rate of less than 0.4%. Thus, accuracy of phishing URL classification can be greatly increased through the use of these statistical measures.
Consistent sparsification for graph optimization
(2013)

In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the pose graph amenable to available processing and memory resources. In particular, in this paper we introduce a consistent graph sparsification scheme: (i) sparsifying nodes via marginalization of old nodes, while retaining all the information (consistent relative constraints) - which is conveyed in the discarded measurements - about the remaining nodes after marginalization; and (ii) sparsifying edges by formulating and solving a consistent ℓ1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data.
The Digital Self-Tuning Control of Step a Down DC-DC Converter
(2013)

A digital self-tuning control technique of DC-DC Buck converter is considered and thoroughly analyzed in this paper. The development of the small-signal model of the converter, which is the key of the control design presented in this work, is based on the state-space averaged (SSA) technique. Adaptive control has become a widely-used term in DC-DC conversion in recent years. A digital self-tuning Dahlin PID and a /Keviczky PID controller based on recursive leastsquares estimation are developed and designed to be applied to the voltage mode control (VMC) approach operating in a continuous conduction mode (CCM). A comparative study of these two digital self-tuning controllers for step change in input voltage magnitude or output load is also carried out. The simulation results obtained using a Matlab SimPowerSystems toolbox to validate the effectiveness of the proposed strategies are also given and discussed extensively.